PlasmoData.jl -- A Julia Framework for Modeling and Analyzing Complex Data as Graphs
This provides a domain-specific tool for researchers and engineers to unify data modeling across various formats, though it is incremental as it builds on existing graph theory concepts.
The authors introduced PlasmoData.jl, a Julia framework that models complex datasets as graphs to enable analysis using graph theory and machine learning tools, demonstrating its versatility on image classification, disease outbreak detection, and technology pathway analysis tasks.
Datasets encountered in scientific and engineering applications appear in complex formats (e.g., images, multivariate time series, molecules, video, text strings, networks). Graph theory provides a unifying framework to model such datasets and enables the use of powerful tools that can help analyze, visualize, and extract value from data. In this work, we present PlasmoData$.$jl, an open-source, Julia framework that uses concepts of graph theory to facilitate the modeling and analysis of complex datasets. The core of our framework is a general data modeling abstraction, which we call a DataGraph. We show how the abstraction and software implementation can be used to represent diverse data objects as graphs and to enable the use of tools from topology, graph theory, and machine learning (e.g., graph neural networks) to conduct a variety of tasks. We illustrate the versatility of the framework by using real datasets: i) an image classification problem using topological data analysis to extract features from the graph model to train machine learning models; ii) a disease outbreak problem where we model multivariate time series as graphs to detect abnormal events; and iii) a technology pathway analysis problem where we highlight how we can use graphs to navigate connectivity. Our discussion also highlights how PlasmoData$.$jl leverages native Julia capabilities to enable compact syntax, scalable computations, and interfaces with diverse packages.